export root_dir=${root_directory}
export task=CoLA
export prun_type=train_kd
export rep_loss_type=full_cosine
export block_size=512
export step=19999

for zero_rate in 0.5
do
	for mask_seed in 1 2 3
	do
	{
		for seed in 1 2 3
		do
		{
			python $root_dir/imp_and_fine_tune/glue_trans.py \
			       --dir pre \
			       --mask_dir $root_dir/mask_training/models/prun_bert/unstructured/$prun_type/wikitext-103/length$block_size/$rep_loss_type/mag_init/$zero_rate/seed$mask_seed/step_$step/mask.pt\
			       --output_dir $root_dir/imp_and_fine_tune/log/glue/$prun_type/wikitext-103/$rep_loss_type/steps/length$block_size/step$step/$task/$zero_rate/seed$mask_seed/$seed \
			       --logging_steps 50 \
			       --task_name $task \
			       --data_dir $root_dir/imp_and_fine_tune/glue/$task \
			       --model_type bert \
			       --model_name_or_path bert-base-uncased \
			       --do_train \
			       --do_eval \
			       --do_lower_case \
			       --max_seq_length 128 \
			       --per_gpu_train_batch_size 32 \
			       --learning_rate 2e-5 \
			       --num_train_epochs 3 \
			       --evaluate_during_training \
			       --save_steps 0 \
			       --seed $seed
		}
		done
	}
	done
done
